Assess learners' ability to evaluate and balance trade-offs among patient privacy (e.g., de-identification, differential privacy), model utility (accuracy, calibration, clinical impact), and bias-mitigation strategies (pre/in/post-processing) when deploying ML in healthcare; include regulatory and ethical constraints, fairness metrics, risk–benefit analysis, operational monitoring, and stakeholder communication through applied case scenarios to produce auditable, defensible deployment recommendations.